Comprehensive analysis of ncRNA involvement in brain microglia immunology
Introduction
Microglias are brain-resident myeloid cells with important functions in immune surveillance, mediating innate immune responses in the central nervous system (CNS) [1]. As a critical driver of neuroinflammatory responses, microglia secrete various substances that affect other glial cells and neurons [2] [3], contributing to the development of many neurological disorders such as cerebral ischemia, brain injury, neurodegenerative diseases, and neuroviral infections. In both healthy and diseased CNS, microglias are perpetually exposed to diverse environmental stimuli that can influence their function, corresponding to a unique microglia molecular and morphological profile [4].
Recently, non-coding RNAs (ncRNAs) have been identified and found to play critical roles in the regulation of gene expression [5,6]. Long non-coding RNAs are longer than 200 nucleotides and contribute as the regulators of transcription, the modulators of mRNA processing and unclear domain organization [7,8]. Circular RNAs (circRNA) are endogenous ncRNAs lacking 5′ and 3′ end, which displayed evolutionarily conservation and high stability [9]. MicroRNA (miRNA) is a common type of short ncRNAs and play a role as a negative gene regulator by targeting mRNA 3′-untranslated regions (UTR) causing target genes undergo translation repression or decay [[10], [11], [12]]. Previous studies have confirmed that multiple kinds of ncRNAs have been involved in biological regulation in microglia immune events, such as miR-146 in microglia activation [13], lncRNA-GAS5 and miR-124 in microglia polarization [14,15], and miR-223 in microglia autophagy [16], and miR-98 in microglia neuroinflammation [17]. In the meanwhile, miRNAs and lncRNAs were also involved in microglia-neuronal crosstalk through extracellular vesicles (EV) transfer [18]. Therefore, there is a need to develop a comprehensive platform to explore the involvement of ncRNAs in microglia immune events across multiple experimental models or disease conditions.
To explore the associations between ncRNAs and microglia, we developed an integrated platform named MG-ncRexplorer by integrating manual retrieval and computational detection strategies. MG-ncRexplorer includes 648 low-throughput validated ncRNA-microglia associations, as well as cell lines and tissue expression data from multiple databases of Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and Chinese Glioma Genome Atlas (CGGA). Differential expressed ncRNAs were inspected among different microglia conditions. To demonstrate the usage of MG-ncRexplorer, we constructed three complex ncRNA regulatory networks and identified risk miRNA signatures involved in microglia-glioma crosstalk. To our knowledge, MG-ncRexplorer is the first integrated platform displaying a large-scale view of the associations between microglia and ncRNAs, including miRNAs, lncRNAs and circRNAs, which would be helpful for the exploration of microglia regulatory mechanism and development of clinical therapy procedures accordingly.
Section snippets
Experimental validated ncRNAs associated with microglia
We extracted information about experimentally validated associations between ncRNAs and microglia from PubMed using the keywords ‘microglia and miRNA’, ‘microglia and long non-coding RNA’, ‘microglia and lncRNA’ and ‘microglia and circRNA’.
Through manually screening, we got relationships of microglia and ncRNAs from published articles, which were directly obtained from the Abstract or Results parts. Each association term contained the detailed ncRNA information, the regulatory target of ncRNAs
ncRNA and model summary for manual retrieval associations
Through manually screening, we got a total of 648 regulatory relations between microglia and ncRNAs (including 487 miRNAs, 145 lncRNAs and 16 circRNAs) from 369 published articles. As shown in Fig. 1A, the top 5 frequency lncRNAs were H19, XIST, MALAT1, NEAT1, and SNHG1. The top 5 frequency miRNAs were miR-124, miR-155, miR-146a, let-7a and miR-21. By summarizing all the microglia involved associations, all microglia biological events were divided into four classes, inflammation, polarization,
Discussion
Microglia are highly dynamic innate immune cells that are capable of robust chemotaxis, phagocytosis, antigen presentation, and cytokine production functions. Non-coding RNAs contain multiple classes of RNAs, such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circRNAs. Among these non-coding RNAs, miRNAs are an abundant class of endogenous small non-coding RNAs that consist of 21–25 nucleotides in length. miRNAs have a variety of regulatory functions in cells and orchestrate complex
Conclusions
Large amount of information related to microglia associations is scattered across the published articles, which is inconvenient for researchers to thoroughly study the regulatory mechanisms of ncRNAs in microglia biology and translate their discoveries into clinical practice. Accordingly, we developed an online query and analytical platform MG-ncRexplorer to manage the experimentally validated and computationally detected associations of ncRNA on microglia. The MG-ncRexplorer contained two main
Ethics approval and consent to participate
Not applicable.
Availability of data and materials
The datasets generated and/or analyzed during the current study are available in the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/), The Cancer Genome Atlas (https://portal.gdc.cancer.gov/) and Chinese Glioma Genome Atlas (http://www.cgga.org.cn/) repository, and we also develop a database, MG-ncRexplorer (bio-bigdata.hrbmu.edu.cn/MG-ncRexplorer), for users exploring ncRNA-microglia associations.
Consent for publication
Not applicable.
Funding
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62101164, 62172131).
Authors' contributions
Chunlong Zhang, Feng Li and Xiaoling Zhong analyzed and interpreted the data. Chunlong Zhang, Ziyan Zhao, Yanjun Xu and Feng Li performed the bioinformatics analyses. Yunpeng Zhang, Xiaoling Zhong, Guiyuan Tan and Yunpeng Zhang performed the biological evaluation. Chunlong Zhang and Yanjun Xu wrote the manuscript. Feng Li constructed the website platform. All authors read and approved the final manuscript.
Declaration of Competing Interest
The authors declare that they have no competing interests.
Acknowledgements
Not applicable.
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These authors contributed equally to this work.